Salleh Hussain (1,2), Fergus R. McInnes (1), Mervyn A. Jack (1)

A hybrid neural network is proposed for speaker verification (SV).
The basic idea in this system is the usage of vector quantization
preprocessing as the feature extractor. The experiments were carried
out using a neural network model(NNM) with frame labelling
performed from a client codebook known as NNM-C. Improved
performance for NNM-C with more inputs and proper alignment of
the speech signals supports the hypothesis that a more detailed
representation of the speech patterns proved helpful for the system.
The flexibility of this system allows an equal error rate (EER) of
11.2% on a single isolated digit and 0.7% on a sequence of 12 isolated
digits. This paper also compares neural network speaker verification
system with the more conventional method like Hidden Markov
models.